Special Issue Part 1 (Issue 3) and Part 2 (Issue 4) of AIEDAM are based on a workshop on Learning and Creativity held at the 2002 conference on Artificial Intelligence in Design, AID '02 (www.cad.strath.ac.uk/AID02_workshop/Workshop_webpage.html; Gero, 2002), the sixth of similar workshops, with the previous five focusing on Machine Learning in Design and being held at AIDs '92, '94, '96, '98, and '00 (Gero, 1992, 2000; Gero & Sudweeks, 1994, 1996, 1998). The first three workshops also resulted in special issues of AIEDAM (Maher et al., 1994; Duffy et al., 1996, 1998).

In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning, and involvement of an agent. Three types of links between team design and collective learning were identified, namely teleological, rationale, and epistemic. Hypotheses of collective learning are made based upon existing theories and models in design and learning, which were tested using a protocol analysis approach. The model of collective learning in design is derived from the test results. The proposed model can be used as a basis to develop agent-based learning systems in design. In the future, collective learning between design teams, the links between collective learning and creativity, and computational support for collective learning can be investigated.

This paper describes how a computational system for designing can learn useful, reusable, generalized search strategy rules from its own experience of designing. It can then apply this experience to transform the design process from search based (knowledge lean) to knowledge based (knowledge rich). The domain of application is the design of spatial layouts for architectural design. The processes of designing and learning are tightly coupled.

This paper describes an approach to the analysis of design concepts (DCs) using the rough set theory. The proposed approach attempts to extract design knowledge from past designs, and used the knowledge obtained to perform DC–capability mapping in a dynamic design evolution environment. The mapping enables designers to estimate the feasibility of a DC to meet stipulated design specifications. The proposed approach encompasses two algorithms, namely, the dissimilar objects algorithm and the attribute decomposition algorithm, to deal with an information system with unavailable information and multidecision attributes, respectively. The details of these algorithms are presented. A case study on the design of vacuum cleaners is used to illustrate the capability of the proposed approach.